AE8 GROUP7 FINAL REPORT

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Lê Trần Công Thành
Nghiêm Xuân Bảo
Bùi Nguyễn Khánh Hà
Đặng Hoài Thanh Trúc
Lê Thị Xuân Quỳnh

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31211023652
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Unit name: Applied Econometrics
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AE_S1(2022_2023)DH47IS
B-8

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Final Report

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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

Note: An examiner or lecturer / tutor has the right to not mark this assignment if the
above declaration has not been signed.

THE RELATIONSHIP
BETWEEN PERCEIVED
CORRUPTION AND FDI IN
THE G20 COUNTRIES IN THE
PERIOD OF 2012-2021

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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

ABSTRACT
This paper examines the impacts of perceived corruption on FDI in various countries over
the 2012-2021 period. This study also discusses whether trade openness affects such
relationships. By using multiple regression analysis with the data of 200 observations,
including countries in the G20, we found that corruption has a negative relationship with
foreign direct investment. Our empirical results also reveal that there is significant evidence
to conclude the impact of trade openness on the differences of these relationships.

KEYWORDS: corruption, FDI, trade openness, G20

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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

Table of Contents

1. Introduction
a. Overview of the problem
A thriving field of investigation on the FDI and Corruption’s connection has emerged
over the past three decades as a result of a rising FDI sector and novel FDI recruiting
techniques in countries all over the world. From $58 billion in 1982 to $1540 billion in
2019, FDI inflows rose. FDI outflows totaled $1314 billion and $27 billion, respectively.
(Luu et al, 2019) This study will offer data and bits of evidence to back up the claim that
FDI contributes to corruption worldwide, particularly in developing nations.

b. Research Gap
Even though the connection between corruption and FDI has been well investigated,
the literature is still not clear. Globally, research on the connection between FDI,
corruption, and company success has a long history and has generated contentious
discussion (Krifa et al, 2022). While some research indicated that corruption discourages
FDI, others came to the opposite conclusion.Therefore, FDI may be and usually is a key
driver of economic growth and job creation for many local and national businesses.
Moreover, corruption's concealment and illegality cause major market distortions and
uncertainty in the corporate world.

c. Research Question
Despite the fact that there are several theoretical and empirical research on FDI,
unexpectedly, their sample sizes only contain businesses in developed nations, which
continues to spark numerous public discussions in other areas (Chang et al, 2011).
Additionally, relatively little study has been done to determine how inward FDI affects
businesses in the countries that receive such direct investments (Gracia et al, 2013) The
relationship between FDI and company performance was specifically examined in the
enlarged EU and was thought to have favorable direct benefits but restricted size (Bruno
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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

et al., 2014). It is crucial for managers and policymakers to comprehend FDI's influence,
particularly on productivity, as it is not only seen as an engine for economic growth but
also forces businesses to fight for customers. We thus aim to investigate the unique
impact of FDI influx on corruption globally as the primary view of this study.

d. Motivation
By investigating whether and how inbound FDI and the prevention of corruption
impact the performance of nations throughout the world, this study hopes to partially
settle the argument. We investigate the effects of FDI inflow and the degree of
corruption control on a firm's ROA and sales growth rate using data from the World Bank
for the years 2012 to 2021. Additionally, compared to earlier times, our civilization is
richer now. The economic freedom system has ensured universal affluence (Lemicux et
al, 2018). Because of this, we also ask if FDI and corruption have distinct consequences
in other nations and continents, especially in more diverse and developed countries like
G20. For all of the reasons that have been listed above, this assignment examines the
relationship between FDI and Perceived Corruption to answer the question: Is the

impact of corruption on FDI significant?

e. Contributions of the proposal
The study adds to the body of knowledge. The effects of inward foreign investment
and the rate of corruption control are novel additions to the body of knowledge and the
field of business development, respectively. First, we provide international evidence
about the influence of FDI and the control of corruption on G20 performance
specifically. The fact that we divide and examine the various impact levels of FDI and
corruption control among nations with high and poor economic freedom is specifically
another significant discovery. Finally, by offering insightful information on FDI and
corruption and generating a fresh perspective on their relationship, this article may be
utilized as a reference for individuals who are interested in learning more about the
pertinent subjects.

f. Summary hypothesis
In our research, we have found two hypotheses. First, in particular for the top
percentile of FDI stock distributions, the effect of corruption on FDI may not be
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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

detrimental. However, if a country is selected as the host country, a higher level of
corruption would not deter FDI, according to the parametric analysis, which
demonstrates that higher levels of corruption would deter FDI otherwise (Barassi and
Zhou, 2012). And with all the studies before, we suppose that low corruption levels
positively influence foreign direct investment. Second, we continue to investigate the
connection between corruption and openness. Trade openness, which is influenced by
import, export intensity, and tariff barriers, has a negative impact on corruption (Larran

& Tavares, 2000). Regardless of the choice of a functional form, the corruption index,
and the inclusion of additional pertinent factors, both the quality (the extent of trading
partners' corruption) and the level of openness (total trade estimated by imports and
exports) have significantly constrained corruption (Gokcekus & Knörich, 2006). By that
means, we came up with the hypothesis of the negative impact of corruption on FDI is
weaker in countries with high openness.

g. Summary Empirical Result
This study reviewed the situation of corruption in G20 countries, over a period of
9 years, from 2012 to 2021, and the effect of it on those countries’ FDI. The empirical
results of our investigation bring out the conclusion that corruption goes the opposite
way with foreign direct investment, and the coefficients are statistically significant in
almost all cases. And also, the effect of perceived corruption on FDI is less
pronounced for countries with high levels of trade openness. We have found that there
are implications for these results. With the perceived corruption in G20 countries,
these governments should not only focus on controlling corruption, but also have to
improve on other variables. By that means, they can enlighten the relationship
between FDI and corruption in G20 countries.
The results of this study are shown below. The literature review is evaluated in
Section 2 and our hypotheses are developed. Section 3 provides details on the
information and methodology. Section 4 presents empirical findings. Section 5
presents our conclusion.

2. Literature review and hypothesis development
a.
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Overview of the problems and prior studies

The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

When someone or a group of people in a position of authority engages in corruption,
they are engaging in a type of dishonesty or an offence with the intent to enrich
themselves or their organisation by abusing their position of authority. Additionally, we
might describe "corruption" as dishonest or unlawful behaviour, particularly by influential
persons (such as government officials or police officers). Bribery, influence peddling,
and embezzlement are just a few of the various behaviours that corruption may
encompass. It may also involve actions that are lawful in many nations. Over the past
three decades, a growing area of study on the connection between corruption and FDI
has evolved as a result of an expanding FDI industry and cutting-edge FDI recruiting
strategies in nations all over the world. More specifically, several prior studies and
theories have been developed to explain the relationship between these two elements.
For instance, one of the factors influencing the location of FDI has recently been added:
the level of corruption in the host country. According to Mudambi, Navarra & Delios
(2013), the literature holds that corruption is a factor that determines FDI on its own
(Habib & Zurawicki, 2002; Voyer & Beamish, 2004), whereas, actually, the cost of
government regulation affects both FDI and corruption (Rose-Ackermam, 1999; Shleifer
& Vishny, 1999). This article examines the connection between corruption and FDI
flows to the Middle East and North Africa in order to determine whether corruption is
more significant than other FDI factors, in addition to another journal's attempt to take
another step because the data on the causal relationship between corruption and FDI
remained ambiguous (Helmy, 2013).
b.

Theories to support the research question
Using parametric and non-parametric techniques, a European journal on political
economy found that corruption generally has a very negative impact on the likelihood of
FDI. Additionally, they show that the impact of corruption on the stock of FDI is not

consistent. After accounting for other pertinent factors including MNEs' site preferences,
market size, and factor costs between 1996 and 2003, the impact of corruption on FDI
may not be negative, particularly for the top percentile of FDI stock distributions.
Meanwhile, the parametric analysis shows that a higher degree of corruption would
discourage FDI, but that if a nation is chosen as the host country, a higher level of
corruption would not discourage FDI (Barassi and Zhou, 2012). Another article
researching evidence on corruption as an incentive for foreign direct investment implies
that perceived corruption may, over time, account for up to 40% of the observed global
FDI increase in the nations in their dataset. FDI from nations with a reputation for

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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

corruption or weak institutions is becoming more significant. These investments are
frequently made in nations with similar levels of corruption. Both the amount of
corruption in the host country and the disparity between home and host country
corruption have a negative impact on FDI (Brada, Drabek, Mendez & Perez, 2019). In
addition, according to Tosun and İYIDOĞAN (2014), it is discovered that corruption has
negative long- and short-term effects on foreign direct investment in Turkey, proving the
absence of "helping hand" corruption there. Long-term FDI growth also coincides with
rising income. Contradictory to predictions, it has been found that a rise in political risk
has a short-term positive impact on FDI inflows. The empirical findings show that
corruption is a deteriorating factor that seriously restricts FDI inflows. When the two
main FDI components - greenfield investment and cross-border M&As - are
independently evaluated, this outcome appears to be in contradiction. In particular,
corruption continuously deters cross-border mergers and acquisitions over time, while it
seems to have a beneficial impact on greenfield investments (Luu, Nguyen, Ho and

Nam, 2018). When the GDP per capita is removed from the regression, the amount of
corruption in the host nation has a negative impact on FDI inflows. Nevertheless, the
findings indicate that, in promoting FDI inflows into the country, the GDP per capita as a
proxy for market size and the nation's quality of institutions are more significant than the
amount of corruption (Epaphra & Massawe, 2017). Because it interferes with
investment operations and inflows of foreign direct investment, corruption poses a
significant barrier to economic progress in MENA nations. To stop the corruption
epidemic in this situation, governments must put into place strong anti-corruption
measures (Hakimi & Hamdi, 2017).
c.

Empirical papers to support these theories
The relationship between corruption and the inflow of foreign direct investment
(FDI) is the focus of this essay. The researchers discovered that a lot of earlier
publications on the subject had concentrated on the adverse/beneficial consequences
of corruption on FDI influx after reviewing the prior literature. By raising the amount of
transaction costs and uncertainty, corruption may have a detrimental effect on FDI.
These elements are anticipated to hinder FDI. By "greasing" the wheels of commerce,
corruption may also have a favourable effect on FDI. That is, corruption might
encourage investment by serving as "grease money," allowing investors to get through
red tape and speed up decision-making. Here, the term "money" refers to bribes. This
theory holds true when there is a lack of adequate regulatory oversight, and corruption

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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

may positively affect FDI. For instance, Quazi (2014) examined 1995–2011 panel data

and discovered that corruption had a robustly negative influence on FDI. have looked
into. Over the years 1998 to 2008, the effect of corruption on FDI inflows in 73 countries
was examined (Castro and Nunes, 2012). Because they offer a more favourable
environment for investors, the least corrupt nations may draw more foreign direct
investment (Castro et al, 2013). By applying OLS to panel data from 17 Asian
economies from 1995 to 2009, Alemu (2012) examined the impact of corruption on FDI
influx. According to the findings, a rise in corruption of 1% causes FDI inflow to drop by
about 9.1 percentage points. There were 82 nations represented in the cross-sectional
data, both developed and developing (Ardiyanto, 2012). Using panel data, it was found
that corruption had a negative impact on FDI inflows to rich nations but had a
somewhat positive impact on FDI inflows to developing countries. However, several
scholars have looked into how FDI affects the level of corruption. Lower levels of
corruption are strongly correlated with FDI as a percentage of GDP. The quantitative
effect of FDI on corruption seemed to be on par with the quantitative effect of per capita
GDP on corruption in terms of size.

d.

Main hypothesis
Based on the support of the above theories, this report suggests an important
link between corruption and labour market outcomes through the following hypothesis:
Hypothesis 1: Low corruption level positively influences foreign direct investment.
It is obvious that corruption has a detrimental impact on foreign direct
investment. The result may, however, fluctuate slightly depending on the importance of
trade openness. In terms of trade openness, empirical studies show that if a country
promotes its trade openness, it will lower corruption rates. The strong correlation
between the two factors was also highlighted in the majority of those studies. Torrez
(2002) supported the negative relationship between trade openness and corruption
because quantitative trade limitations cause a shift in economic resources from directly
productive activity to rent-seeking ones, including corruption. Similar conclusions were

found in other reports. Corruption is adversely impacted by trade openness, which is
determined by import, export intensity, and tariff barriers (Larraín & Tavares, 2000).
Both the quality (the extent of trading partners’ corruption) and the level of openness
(total trade estimated by imports and exports) have significantly constrained corruption

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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

regardless of the selection of a functional form, the corruption index, and the inclusion
of additional relevant factors (Gokcekus & Knörich, 2006). Besides, one study offered a
mixed result, saying that in environments with weak institutional frameworks, increased
trade facilitation may be a practical and successful strategy for reducing corruption over
the short term (Shepherd, 2009).
Based on the analysis made by the previously mentioned arguments, the next
hypothesis is established:
Hypothesis 2: The negative impact of corruption on FDI is weaker in countries with
high openness.

Figure 1. Conceptual framework

3. Methodology
a. Data selection
•

Database
The data utilised in this analysis include annual statistics for the G20 nations'

gross domestic product, foreign direct investment, and corruption perception index
score from 2012 to 2021. The statistics for the world development indicators issued by
the OECD are where the FDI and GDP figures are gathered. Besides, the information
on corruption is obtained from Transparency International's official website's corruption
perception index. This study utilised the score as a method to quantify corruption from
the corruption index. The range of scores is 0 to 10. The maximum score, 10, denotes a

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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

country that is significantly free of corruption, while a score of 0 denotes a heavily
corrupt nation.
•

Scope of research and Research Period
This research employs panel data for G20 countries (see Table 1) over the

period 2012–2021. All countries for which data are available over this period are
included in this study.

COUNTRY SAMPLE
Argentina
Australia
Brazil
Canada
China
France

Germany
India
Indonesia
Italy

Japan
South Korea
Mexico
Russia
Saudi Arabia
South Africa
Turkey
United Kingdom
United States
European Union

* European Union includes 27 countries, which are Austria, Belgium, Bulgaria, Croatia,
Republic of Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece,
Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal,
Romania, Slovakia, Slovenia, Spain, and Sweden.

b. Measurement for dependent variable, independent variable and
control variables
§ Measurement for FDI (Dependent variable)
Foreign Direct Investment (FDI) flows record the value of international business
dealings involving direct investment over a specific time period, often a quarter or a
year (FDI flow, 2022). Equity transactions, earnings reinvested, and intercompany loan
transactions make up financial flows. FDI flows are calculated as a percentage of GDP
in USD. FDI forges enduring and reliable ties between nations' economies.
§ Measurement for perceived corruption (Independent variable)

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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

The data set for corruption for this research was procured from Transparency
International. Corruption is measured using the Corruption Perceptions Index (CPI),
which is the most widely used global corruption measurement. This indicator is based
on the opinions and perceptions of corruption among citizens and experts through
surveys. The surveys are conducted with technical expertise to ensure that the data is
representative of the group being investigated (Transparency International, 2016). The
CPI gives a more complete view of the situation in a specific nation than each source
examined separately since it incorporates several distinct manifestations of corruption into one
globally comparable index (Transparency International, 2021).

§ Other Factors Influencing FDI (Control variables)
Apart from perceived corruption, some other factors also affect FDI that we consider
control variables.
(i) GDP growth
GDP is crucial as it gives data on the size and functioning of an economy. The
pace of GDP growth is usually used to evaluate the health of the economy as a whole
(IMF, 2020). To measure how quickly an economy is expanding, the GDP growth rate
looks at a nation's change in economic production from year to year. It has been shown
that FDI flows are often attracted to nations with growing economies (Alemu, 2012.).
Part of the reason for this is that the majority of foreign investors think that an
expanding market will enable more efficient production scale (Agosin & Machado,
2007). Iamsiraroj & Doucouliagos (2015) also supported the idea that FDI growth and
GDP growth are positively correlated.

(ii) Unemployment rate
The unemployment rate refers to the percentage of the labor force that is
unemployed but available and looking for work. Even though the majority of research in
this field claims that FDI lowers the unemployment rate, the connections between these
two phenomena have not yet been thoroughly shown by the collected data. Meanwhile,
it is clear that there are substantial differences among countries in terms of the
relationships between foreign direct investment and employment or the unemployment
rate (depending on the economic structure as well as the type of foreign direct
investment received), as well as over time (the structure of an economy can change
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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

significantly over a long period of time as well as the typology of the received foreign
direct investment can change significantly) (Strat, Davidescu, and Paul, 2015).
(iii) Income per capita
The income per capita, as measured by GDP/capita (PPP), indicates a country's
degree of development as well as the purchasing power of its citizens. As a result,
income per capita is projected to be positively related to FDI inflows (Alemu, 2012).
(iv) Infrastructure
Infrastructure investment encourages changes in the caliber of circumstances,
procedures, and results of economic growth through affecting innovation, industrial
structure, and productivity (Du, Zhang & Han, 2022). Similar to this, Kumar (2006)
showed that effective foreign investment depends on a strong infrastructure. As a
result, infrastructure has a direct role in FDI inflows.
(v) Openness
One of the requirements to draw FDI is thought to be the host nation's level of
openness. To be clear, according to Ang (2008), a 1% point rise in trade openness

results in an increase of 1.094% to 1.323% points in China's FDI inflows. Similarly,
verifiable information shows that mainland China has drawn more than 70% of Taiwan's
foreign investors due to its economy's growing openness. Chen (2011) reports that a
total of 91.7 million USD had been invested.

c. Baseline Model
Based on the previous discussion of the theoretical relationship between
corruption and FDI inflows, and considering other control variables, we can specify the
level of FDI as a function of the level of corruption and the measurements of the
aforementioned control variables. Given the panel structure of the data in this study,
we, therefore, constructed a model to investigate the impact of corruption on FDI
inflows using balanced panel data for G20 countries from 2011 to 2021 as follows:

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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

where index i refers to the unit of observation, t is the time period, log(FDI) it is the
dependent variable: log-transformed annual FDI inflow, CPI it refers to the corruption
index of the host economy, Unemployment rate it is the unemployment rate, given as
percentage, and log(GDP per capita)it denotes log-transformed GDP per capita.
In this formula, we take the logarithm of some variables. The logarithm is a
monotonic function. As a result, when the logarithm is applied to a variable, the
coefficients' values are changed rather than their signs. Additionally, it significantly
lowers heteroscedasticity. We wish to examine the relationships between the variables
in these tests.
The elimination of "zero" numbers and negative foreign direct investment is one
reason for employing the logarithm. There are many instances when there is no direct

investment in a certain nation when examining the entire amount of direct investment,
much less by country of origin. The cause is that emerging nations are really
experiencing a capital outflow from industrialized nations in the form of dividend
payments, even if no investments are being made.

d. The interaction of High Openness and Corruption Perception Index
To investigate the relationship between perceived corruption and high openness
to FDI in the G20 countries in the period of 2012-2021, we consider the following model
specification.

Where subindexes i and t stand for unit of observation and time period,
respectively. All variables are notified in the Baseline model, and we continue to apply
the sample restrictions by the time period and observation units dummies in the model
specifications. To be more specific, the correlation coefficient of the interaction term
CPI*High Openness will explain the effects of corruption and trade openness in the
sake of FDI. If the openness of 20 developed countries tends to have a clear separation
between high and low openness, the coefficient of interaction term, specifically, tends to
be significant and positive.
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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

4. Empirical results
Table 1. Variable Definitions
Description

Source

FDI, Net Inflows (Current $US)

World Bank

Dependent Variable

Foreign Direct
Investment (FDI)

Independent Variable

Corruption Perception Index = 100 - no corrupt
Index = 0 - totally corrupt
Index (CPI)

Transparency

S

Control Variable

GDP growth

World Bank

GDP Growth Annually (%)

Unemployment rate

Share of the labor force that is without work
but available for and seeking employment
(% of total labor force)

World Bank

GDP per capita

A country’s GDP divided by its total
population (current $US)

World Bank

Infrastructure

Mobile cellular subscriptions per 100 people
(subcribers)

World Bank

Openness

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16

The ratio of a country’s total trade, the sum of

exports plus imports, to the country’s GDP
World Bank
(%)


The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

Table 2. Foreign direct investment, Corruption Perception Index (CPI), Unemployment Rate, GDP per
capita, Infrastructure and Openness for the years 2012-2021
Panel A - Countries with High Openness

Panel B - Countries with Low Openness

Unemployment log(GDP
Infrastructure Openness
rate
per capita)

Unemployment log(GDP
rate
per
Countries Obs log(FDI) CPI
capita)

Infrastructure Openness

Countries

Obs log(FDI) CPI

Mexico

10

23.362

29

9.292

9.182

105.626

61.4

Argentina

10

22.774

34

8.699

9.389

139.370

26.7

Canada

10

24.536

74

10.983

10.766

142.448

57.4

Brazil

10

24.941

36

5.634

9.157

108.365

42.4

Germany

10

21.688

73

3.452

10.730

123.095

82.8

China

10

26.117

37

10.603

9.073

113.864

28.2

Italy

10

22.943

44

4.179

10.419

91.748

73.70

Indonesia 10

23.629

33

4.599

8.233

103.145

40

145.933

68.3

44.4

23.081

52

6.046

10.316

South
Africa

79.923

South Korea 10

10

22.277

40

5.706

8.814

Saudi Arabia 10

22.606

45

27.107

10.028

153.245

55.7

Australia

10

24.485

72

4.226

10.966

131.759

42.5

Turkey

10

23.242

39

10.972

9.259

94 894

57.3

France

10

24.044

64

3.166

10.604

134.035

33.4

United
Kingdom

118.947

59.1

157.068

48.3

10

24.194

72

5.269

10.684

Japan

10

23.453

68

5.235

10.596

European
Union

27.1

26.759

50

8.833

10.435

United
States

110.027

10

10

26.432

66

5.720

10.990

India

10

21.366

36

6.998

7.488

85.343

63.8

Russia

10

23.878

26

4.195

9.368

125.056

86.1

116.882

88.9

Unemployment log(GDP
Infrastructure Openness
rate
per capita)
Countries
Countries
High
Openness
Mean
Full Sample

Obs log(FDI) CPI

Unemployment log(GDP
rate
per
Countries Obs log(FDI) CPI
capita)

90

Countries
with Low
Openness
Mean
110

200

17
17

23.601

53.111 9.570

23.888 51.711

10.202
7.943

10.002

124.741
122.485

67.178
53.598

24.239

50

5.954

9.758

Infrastructure Openness

119.728

37.000


The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

Panel C - Means by countries characteristics
log(FDI)

Unemployment rate log(GDP per capita)

CPI

Infrastructure

Openness

Countries with High Openness

23.601

53.111

9.570

10.202

124.741

67.178

Countries with Low Openness

24.239

50

5.954

9.758

119.728

37

Difference

-0.638

3.111

3.616

0.444

5.012

30.178

Notes: The definitions of the variables are provided in Table 1.

Table 2 consists of the descriptive statistics for Foreign Direct Investment, Corruption (as shown by Corruption Perception
Index), Income Per Capita (as shown by GDP Per Capita), Infrastructure and Openness. Panel A and Panel B show values for
countries with high openness and countries with low openness, respectively. In our sample, FDI in countries with high openness is
lower than in countries with low openness. However, there is a situational reversal for Unemployment rate, Income per capita, and
Infrastructure. Moreover, countries with high openness have higher CPI (53.111 compared to 50), which indicates that they are
slightly less corrupt than countries with low openness. Panel C clearly indicates the differences in average for Foreign Direct
Investment, Corruption, Income Per Capita, Infrastructure and Openness between two groups of countries.

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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

Table 3. Descriptive statistics of variables
Notes: The definitions of the variables are provided in Table 1.
The overall sample is an unbalanced panel that consists of 200 country-year observations covering the 10-year period from 2012 to
2021.

Variables

Mean

GDP growth

SD

Minimum

Maximum

3.35

-9.895

10.986

5.289

2.4

33.559

18660.235

1,444

69,288

24.207

68.32

181.790

19.29

21

105

2.039

Unemployment rate
7.546

GDP per capita
26,559

Infrastructure
119.039

Openness
54.475

The table provides the descriptive statistics of control variables. We can specify the standard deviation, minimum, maximum,
mean, as well as the range of each control variable based on this. For instance, the mean of Openness is 54.475, the standard
deviation is 19.29, the minimum is 21 and the maximum equals 105.
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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

Table 4. Correlation coefficient matrix

(1)

(2)

(3)

(4)

(5)

CPI (1)

1.0000

GDP Growth (2)

-0.1319***
0.0627

1.0000

Unemployment
Rate (3)

-0.2011*
0.0043

-0.1454**
0.0400

1.000

GDP per capita
(4)

0.7963*
0.0000

-0.2455*
0.0197

-0.2111*
0.0027

1.0000

Infrastructure
(5)

-0.1683**
0.0172

-0.2286*
0.0011

0.2302*
0.0010

0.0783
0.2702

1.0000

Openness (6)

0.1356***
0.0555

0.0026
0.9713

-0.0274
0.6998

0.2430*

0.0005

-0.0465
0.5132

(6)

1.0000

*Significance at the 1% level.
**Significance at the 5% level.
***Significance at the 10% level.

The pairwise correlation values between independent variables are displayed in Table 4. The positive coefficient between GDP per
capita and CPI represents the negative relationship between them, which means: A higher level of GDP per capita results in a lower
level of corruption in a country. In a journal published in 2020, Moiseev, Mikhaylov, Varyash and Saqib assert that the decline of
corruption results from a society's gain in wealth rather than the other way around (increasing GDP per capita while lowering
corruption), as stated Mustapha (2014). Additionally, in a panel data framework, Mustapha (2014) conducts a number of statistical
tests to show that the corruption index has a negative impact on GDP per capita. Similarly, we can easily find that a nation with
higher openness has lower corruption, as the coefficient between them is positive. The Journal of International Trade & Economic
Development published in 2010 also used the simple regression line to show a negative relationship between openness and
corruption. This could be, again, considered as evidence supporting our second hypothesis
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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

Table 5. Corruption and FDI

Dependent variable: Log (FDI)
CPI

-0.059***
(0.010)

GDP growth

0.201***
(0.050)

Unemployment rate

0.023
(0.021)

GDP per capita

1.791***
(0.213)

Infrastructure

-0.018***
(0.005)

Openness

-0.026***
(0.005)

Constant

12.019***
(1.721)

Year fixed effect

Yes

Observations

200

Adjusted R2

0.2937

*Significance at the 10% level
**Significance at the 5% level
***Significance at the 1% level
Table 5 reports the impact of corruption on FDI and partially supports our first
hypothesis: Low corruption level positively influences Foreign direct investment. As far as we
know, if the slope is negative, then there is a negative linear relationship between two
variables. In this case, we find that corruption goes the opposite way with foreign direct
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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

investment and the coefficients are statistically significant in almost all cases. Specifically,
the slope parameter of CPI is -0.059 indicating that the FDI will decrease by 0.059 if the CPI
index raises 1 score. A similar interpretation can be applied to other variables in the table.
According to a journal written in 2006, a negative correlation between the CPI and FDI has
to be interpreted as a positive relationship between corruption and FDI (Egger & Winner,
2006).

Table 6. The interaction between corruption and high openness
Dependent variable: Log (FDI)
CPI

-0.084***
(0.014)

CPI* High Openness

0.023*
(0.012)

High Openness

-2.522***
(0.697)

GDP growth

0.188***
(0.048)

Unemployment rate

-0.009
(0.021)

Log GDP per capita

1.863***
(0.209)

Infrastructure

-0.022***
(0.005)

Constant

12.504***
(1.655)

Year fixed effects

Yes

Observations

200

Adjusted R2

0.347

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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

*Significance at the 10% level
**Significance at the 5% level
***Significance at the 1% level

Next comes the second hypothesis: The negative impact of corruption on FDI is
weaker for countries with high openness. Table 6 shows the interaction terms
between a dummy for trade openness and corruption. Based on its median, we
categorize the amount of trade openness as high or low. Countries with trade
openness scores higher than 56.5 are considered to have high openness.

As

anticipated, the interaction term's slope parameter is negative and significant at the
1% level. The results can be interpreted as follows: Holding all other factors fixed,
the effect of perceived corruption on FDI is less pronounced for countries with high
levels of trade openness. Again, evidence supports our hypothesis.

5.

Conclusion

Foreign direct investment and corruption control have been topics of current

interest, especially in the research field. This leads to prompt attention for
researchers to look at the factors that influence corruption and FDI. The paper
examines a theoretical framework that links the relationship between FDI and
corruption in G20 countries. With the process of examining journal paper and data,
it has been found that corruption has a detrimental effect on FDI of G20. This
research has consistent results with other findings. We collected data from 200
observations in 20 countries in the G20, all of which are developed countries, during
the period from 2012 to 2021. We applied multiple regression models to test the
effect of corruption and other control variables on the FDI of G20 countries.
Firstly, with hypothesis 1, we discover that corruption has a negative relationship
with foreign direct investment, and the coefficients are almost always statistically
significant. A low level of corruption has a beneficial impact on foreign direct
investment, according to Table 5, which only partially verifies our initial premise. The
slope parameter of the CPI is specifically -0.059, meaning that if the CPI index
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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

increases by 1 score, the FDI will also fall by 0.059. Other variables in the table can
be interpreted in a manner similar to this.
Secondly, with the second hypothesis, after examining and analyzing the
regression. We have come to the conclusion that the negative impact of corruption
on FDI is weaker for countries with high openness, as the results have been shown
in table 6. The interaction term's slope parameter is negative and significant at the
1% level, indicating directly that the effect of perceived corruption on FDI is less
pronounced for countries with high levels of trade openness.

6. Highlight

1. Low corruption level positively influences Foreign direct investment.
2. The negative impact of corruption on FDI is weaker for countries with high openness

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The relationship between Perceived Corruption and FDI in the G20 countries in the period of 2012-2021

7.

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